AMBIENTUM BIOETHICA BIOLOGIA CHEMIA DIGITALIA DRAMATICA EDUCATIO ARTIS GYMNAST. ENGINEERING EPHEMERIDES EUROPAEA GEOGRAPHIA GEOLOGIA HISTORIA HISTORIA ARTIUM INFORMATICA IURISPRUDENTIA MATHEMATICA MUSICA NEGOTIA OECONOMICA PHILOLOGIA PHILOSOPHIA PHYSICA POLITICA PSYCHOLOGIA-PAEDAGOGIA SOCIOLOGIA THEOLOGIA CATHOLICA THEOLOGIA CATHOLICA LATIN THEOLOGIA GR.-CATH. VARAD THEOLOGIA ORTHODOXA THEOLOGIA REF. TRANSYLVAN
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Authors: GEORGE CIUBOTARIU, LIANA MARIA CRIVEI.
DOI: 10.24193/subbi.2019.2.03
Published Online: 2019-12-30
Published Print: 2019-12-30
pp. 34-48
VIEW PDF: PDF
Educational Data Mining is an attractive interdisciplinary domain in which the main goal is to apply data mining techniques in educational environments in order to offer better insights into the educational related tasks. This paper analyses the relevance of two unsupervised learning models, self-organizing maps and relational association rule mining in the context of students’ performance prediction. The experimental results obtained by applying the aforementioned unsupervised learning models on a real data set collected from Babeș-Bolyai University emphasize their effectiveness in mining relevant relationships and rules from educational data which may be useful for predicting the academic performance of students.
Keywords: Educational data mining, Unsupervised learning, Self-organizing map, Relational association rule.
2010 Mathematics Subject Classification. 68T05, 68P15.
